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Investigation of Few-Layer Graphene Ubiquitin Interactions Optical Spectroscopy #WorldResearchAwards

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Introduction Understanding protein–nanoparticle interactions is a rapidly advancing area of research due to its profound implications in biomedicine , biosensing , and nanotechnology . Among various nanomaterials, few-layer graphene (FLG) has attracted significant attention because of its unique physicochemical properties , high surface area, and biocompatibility . Proteins interacting with nanomaterials often undergo conformational changes that can alter their biological function. Ubiquitin , a small yet functionally critical protein involved in protein degradation , signaling, and cellular regulation , serves as an excellent model to study these interactions. Investigating FLG–ubiquitin complexes provides valuable molecular-level insight into how nanomaterials can be safely integrated into biological systems without compromising protein structure or function. Rationale for Studying FLG–Ubiquitin Interactions Ubiquitin’s compact structure, well-defined secondary elements, and biol...
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Optimization of T6 Heat Treatment for EV31A Magnesium Alloy Performance #WorldResearchAwards

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Introduction The optimization of heat treatment routes is critical for unlocking the full potential of rare-earth-containing magnesium alloys for high-performance structural applications. EV31A magnesium alloy , strengthened by Nd, Gd, Zn, and Zr additions , has attracted significant attention due to its promising elevated-temperature mechanical properties. This study focuses on systematically optimizing the T6 heat treatment parameters to establish clear correlations between processing, microstructural evolution, and mechanical performance, aiming to enhance both strength and ductility for advanced engineering applications. Experimental Methodology A comprehensive experimental framework was employed to evaluate the effects of T6 heat treatment on EV31A alloy. Differential scanning calorimetry (DSC) was used to identify suitable solution treatment temperatures, while optical microscopy , SEM-EDS , XRD , and TEM provided multi-scale characterization of phase constitution and microstr...
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Progress in Charge Transfer in 2D Metal Halide Perovskite Heterojunctions Review #WorldResearchAwards

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Introduction Metal halide perovskite ( MHP )-based heterojunctions have emerged as a transformative platform in optoelectronic materials research due to their layered crystal structures, tunable band gaps, and outstanding light–matter interaction. When combined with two-dimensional (2D) materials , these heterojunctions enable precise control over interfacial charge transfer , which is essential for high-performance optoelectronic devices. Understanding the fundamental mechanisms governing carrier generation, separation, and recombination at these interfaces is therefore a central research focus. Charge Transfer Mechanisms at MHP/2D Interfaces At the core of MHP-based heterojunction performance lies interfacial charge transfer. Band alignment between MHPs and 2D materials such as graphene , MoS₂, and WS₂ facilitates efficient electron–hole separation. Type-II band structures are particularly effective, allowing electrons and holes to migrate into different layers, thereby extendin...
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Pioneer Researcher Award | Celebrating Global Innovation & Excellence #WorldResearchAwards

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Introduction The Pioneer Researcher Award represents a distinguished international honor dedicated to recognizing visionary researchers whose work has redefined scientific understanding and expanded the frontiers of knowledge. Positioned under the Global Particle Physics Excellence Awards, this recognition celebrates originality, sustained research excellence, and transformative contributions that influence global scientific progress. The award serves as a benchmark of credibility, integrity, and innovation in contemporary research culture. Research Excellence and Originality At the core of the Pioneer Researcher Award lies an emphasis on groundbreaking research originality and long-term scholarly excellence. It acknowledges researchers who consistently produce high-impact publications, introduce novel theories or methodologies , and challenge conventional paradigms . These contributions significantly advance their respective disciplines and set new standards for future research ende...
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Density and Viscosity of Orange Oil and Turpentine Biofuels 🌱 #WorldResearchAwards

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Introduction Biofuels are increasingly recognized as sustainable alternatives to fossil fuels due to their renewable origin, reduced greenhouse gas emissions , and compatibility with existing energy systems. Among emerging biofuel components, orange oil , turpentine , and their hydrogenated derivatives have gained attention because they are derived from biomass resources and industrial by-products. Understanding their physicochemical properties is essential for evaluating their feasibility in energy applications. This research focuses on generating reliable experimental data to support the potential integration of these essential oils into biofuel formulations. Experimental methodology and measurement accuracy Accurate determination of density and viscosity is critical for assessing fuel performance in internal combustion engines . In this study, densities and viscosities of orange oil, turpentine, hydrogenated orange oil , and hydrogenated turpentine were measured at atmospher...
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Practical test-time domain adaptation for industrial condition monitoring #worldresearchawards

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  Introduction Machine learning has become a cornerstone of modern industrial analytics , particularly in condition monitoring systems that rely on sensor data for fault detection and health assessment . Despite strong performance during development, many models suffer significant degradation when deployed in real-world environments due to domain shift , where operational conditions differ from training settings. This challenge motivates the need for adaptive, practical, and deployment-ready learning frameworks that can sustain reliability without continuous manual intervention. Domain shift challenges in industrial condition monitoring Industrial condition monitoring systems operate under highly dynamic environments involving varying loads, speeds, sensor types, hardware configurations, and environmental noise. These variations induce domain shifts that violate the assumptions made during model training, leading to increased false alarms or missed fault detections. Traditiona...
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Attention-guided multi-task learning for fault detection in power systems #worldresearchawards

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Introduction Timely and accurate fault diagnosis is a cornerstone of modern power transmission system operation, directly influencing grid stability , safety, and service continuity. With increasing system complexity and penetration of intelligent devices , conventional single-task diagnostic approaches often fall short in meeting real-time and accuracy requirements. This research addresses these challenges by proposing a unified deep learning framework that integrates fault identification, fault type classification, and fault location estimation into a single multi-task learning paradigm , tailored for realistic transmission network conditions . Multi-task learning framework for fault diagnosis The proposed framework adopts a multi-task learning (MTL) architecture that enables simultaneous learning of multiple fault-related objectives within a shared representation space. By leveraging common features across tasks, the model reduces redundancy and improves generalization compared...
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